Photosensitive regions of a typical silicon-based image sensor (e.g., complementary-metal-oxide-semiconductor (“CMOS”) or CCD) are, like panchromatic film, sensitive to all wavelengths in the visual spectrum. While this is sufficient for gray scale image sensors that acquire black-and-white luminance image, color-sensing image sensors require additional optical components to isolate the different spectral bands in the visual spectrum. Such optical components may include a prism that separates the spectral components of visual light or a color filter array (“CFA”), such as a Bayer Pattern CFA, which isolates the color components using pigmented absorption filters. CFAs have become the most dominate color image sensor technology. However, conventional CFAs inherently capture incomplete color samples, which require a demosaicing algorithm to reconstruct the full color image from the incomplete dataset. Demosaicing inherently sacrifices image sharpness and may introduce image artifacts. Since CFAs use pigmented absorptive filters, a significant portions of the incident photons are absorbed in the CFA, thereby lowing the quantum efficiency of the image sensor.
Another technology for implementing color image sensors is referred to as the Foveon vertical color filter image sensor. The Foveon image sensor has three layers of photo-detectors embedded within silicon and stacked vertically to take advantage of the fact that red, green, and blue light are absorbed by silicon at different rates and as result statistically penetrate silicon to different depths. The Foveon image sensor takes advantage of this wavelength to depth penetration relationship to capture full color images at every pixel location of the image sensor. By doing this, the demosaicing process can be avoided, potentially resulting in sharper images. However, the Foveon image sensor suffers from significant spectral overlap between spectral responses of different color photo-detectors within a given multi-pixel stack.
In the Foveon image sensor, the first photo-detector layer absorbs the most blue light signal, a moderate amount of green light signal, and the least amount of red light signal. The second photo-detector layer absorbs a moderate amount of blue light signal, the most green light signal, and a moderate amount of red light signal. The third photo-detector layer absorbs the least amount of blue light signal, a moderate amount of green light signal, and the most red light signal. As such, there is a significant crosstalk between the color signal channels captured at each of the three photo-detector layers. A post-processing color correction matrix is applied against the captured color image data to compensate for the spectral overlap in the responses of the photo-detectors in the three layers. However, this color correction matrix may deteriorate the image quality (e.g., by increasing the noise in the output data).
Organic image sensors are yet another color image sensor technology. Organic image sensors make use of organic photoelectric conversion materials to absorb photons at different wavelengths in each layer and convert these photons into electrons. This technology has yet to mature with current disadvantages including low quantum efficiency, unknown image quality, and issues with high-volume manufacturing and reliability.